Sensing the use of radio frequencies, identifying the users, and potentially jamming their future use are becoming increasingly important in many fields. Such Radio Frequency (RF) sensing and jamming techniques are being used in military applications, law enforcement, and commercial applications. Military applications include Electronic Warfare (EW), such as (a) Electronic Situation Awareness (ESA) (e.g., mapping all the RF signals present in a given region), (b) Electronic Attack (EA) (e.g., (i) deactivation of IEDs and (ii) jamming enemy communications), and (c) Electronic Protection (EP) (e.g., identifying friendly communication being jammed and protecting it from being jammed by changing the frequency). Just RF sensing alone can also be used for many military applications, such as eavesdropping on enemy communication, Signal Intelligence (SIGINT) and Communication. Intelligence (COMINT). Law enforcement applications may include disrupting communications of criminals in drug raids and terrorists, and dealing with hostage situations. Commercial applications may include prevention of corporate espionage.
Several systems have been developed for RF EW and some commercial products are available. These systems are based on energy detectors, maximum likelihood based classifiers, and a signal generator that jams the identified source. However, conventional systems are unable to automatically adapt to the environmental conditions, cannot handle signals that are unknown to the system, and are developed for the detection and classification of only certain broad classes of RF signals. Furthermore, conventional systems often have limited adaptation capability, which if available, is typically handled by manual adjustment. These systems are able to collect data when they encounter signals that the system is not trained for. However, such data is unusable until the systems are retrained in the laboratory and redeployed. Such a process takes months. By then new signals/threats may appear in the field of operations.
In particular, the accurate detecting of signals in a noisy environment has proven to be a difficult task. This task is made especially difficult as noise and/or interference, each of which may or may not be known, may vary with time.